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OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement — arXiv:2610.07847

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07847
License (as archived): CC0-1.0
Archived: 2026-10-07T11:23:14.060Z
SHA-256 of the archived text: cc89c266179919dced0b647c1a5be5b3888236168d3590979ec3942cf5a66dd9
Stable URL of this page: https://metraipolis.com/documento/dc_7df013cae8cf

How to cite

OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement — arXiv:2610.07847 — arXiv (en), https://arxiv.org/abs/2610.07847 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7df013cae8cf (Metraipolis archive dc_7df013cae8cf, sha256 cc89c266179919dced0b647c1a5be5b3888236168d3590979ec3942cf5a66dd9)

BibTeX

@misc{dc7df013cae8cf,
  title = {OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement — arXiv:2610.07847},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07847},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7df013cae8cf; sha256 cc89c266179919dced0b647c1a5be5b3888236168d3590979ec3942cf5a66dd9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07847. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed decision-making. Yet omission bias remains underexplored in LLM evaluation, with the few existing studies limited in scale and focused largely on utilitarian-deontological conflicts. To address this gap, we introduce OMIT, a benchmark consisting of 218 paired-frame scenarios across 10 conflict types, constructed by leveraging disagreement patterns from an LLM-based, five-perspective philosophical persona panel (utilitarianism, deontology, virtue ethics, care ethics, and contractualism). Evaluating eight LLMs, we find that omission bias is pervasive but inversely correlates with model size within families. We further evaluate four inference-time interventions and find that interventions encouraging models to consider moral principles before committing to a yes/no answer reduce omission bias and increase frame-consistent responses, although lower omission bias rates can also coincide with shifts toward action-biased responses. Ultimately, this work contributes not only the OMIT benchmark, but also a methodology for using diverse philosophical disagreement signals to evaluate framing-sensitive inaction preferences and the distributional effects of mitigation attempts in LLMs under complex moral conflicts.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.07847 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:39.435Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).